Personalized Diagnostic Modal Discovery of Traditional Chinese Medicine Knowledge Graph

Xie, Ying; Yan, Chang; Zhang, Dezheng

doi:10.1109/fskd.2018.8687229

Cited by 7 publications

(3 citation statements)

References 1 publication

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“…In this way, we could reinforce the connectivity of TCM knowledge and support the automated diagnosis method. Xie et al [19] proposed a personalized diagnostic pattern mining method based on the TCM knowledge graph with a specific doctor's clinical records. Meanwhile, Yu et al [20] and Zheng et al [21] described the construction of a TCM knowledge graph using databases and documents.…”

Section: Introductionmentioning

confidence: 99%

Traditional Chinese Medicine Automated Diagnosis Based on Knowledge Graph Reasoning

El‐Shafai¹,

Mahmoud²,

El‐Rabaie³

et al. 2022

Computers, Materials &Amp; Continua

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Syndrome differentiation is the core diagnosis method of Traditional Chinese Medicine (TCM). We propose a method that simulates syndrome differentiation through deductive reasoning on a knowledge graph to achieve automated diagnosis in TCM. We analyze the reasoning path patterns from symptom to syndromes on the knowledge graph. There are two kinds of path patterns in the knowledge graph: one-hop and two-hop. The one-hop path pattern maps the symptom to syndromes immediately. The two-hop path pattern maps the symptom to syndromes through the nature of disease, etiology, and pathomechanism to support the diagnostic reasoning. Considering the different support strengths for the knowledge paths in reasoning, we design a dynamic weight mechanism. We utilize Naïve Bayes and TF-IDF to implement the reasoning method and the weighted score calculation. The proposed method reasons the syndrome results by calculating the possibility according to the weighted score of the path in the knowledge graph based on the reasoning path patterns. We evaluate the method with clinical records and clinical practice in hospitals. The preliminary results suggest that the method achieves high performance and can help TCM doctors make better diagnosis decisions in practice. Meanwhile, the method is robust and explainable under the guide of the knowledge graph. It could help TCM physicians, especially primary physicians in rural areas, and provide clinical decision support in clinical practice.

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Section: Introductionmentioning

confidence: 99%

Traditional Chinese Medicine Automated Diagnosis Based on Knowledge Graph Reasoning

El‐Shafai¹,

Mahmoud²,

El‐Rabaie³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…In recent years, as the maturity of technology and more emphasis on healthcare, it has become more and more popular to apply knowledge graph in the medical field and has attracted much attention from researchers in computer and medical to combine these two fields [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26]. Rotmensch et al [11] in 2017 proposed learning a health knowledge graph from electronic medical records by using probabilistic.…”

Section: Introductionmentioning

confidence: 99%

“…Zheng et al [17] put forward a dedicated knowledge graph benchmark for biomedical data mining in 2020. Also, medical knowledge graph can be applied in specific departments, for example, Xie et al [24] in 2018 presented a data-driven traditional Chinese medicine knowledge discovery method based on a knowledge graph. Li et al [18] came up constructing a knowledge graph of knee osteoarthritis in Chinese using an automatic approach in 2020.…”

Section: Introductionmentioning

confidence: 99%

Medical Knowledge Graph in Chinese Using Deep Semantic Mobile Computation Based on IoT and WoT

Liu

Yin

Wang

et al. 2021

Wireless Communications and Mobile Computing

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In this paper, a novel medical knowledge graph in Chinese approach applied in smart healthcare based on IoT and WoT is presented, using deep neural networks combined with self-attention to generate medical knowledge graph to make it more convenient for performing disease diagnosis and providing treatment advisement. Although great success has been made in the medical knowledge graph in recent studies, the issue of comprehensive medical knowledge graph in Chinese appropriate for telemedicine or mobile devices have been ignored. In our study, it is a working theory which is based on semantic mobile computing and deep learning. When several experiments have been carried out, it is demonstrated that it has better performance in generating various types of medical knowledge graph in Chinese, which is similar to that of the state-of-the-art. Also, it works well in the accuracy and comprehensive, which is much higher and highly consisted with the predictions of the theoretical model. It proves to be inspiring and encouraging that our work involving studies of medical knowledge graph in Chinese, which can stimulate the smart healthcare development.

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State-element-aware syndrome classification based on hypergraph convolutional network

Teng,

Ma,

et al. 2024

Expert Systems with Applications

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Personalized Diagnostic Modal Discovery of Traditional Chinese Medicine Knowledge Graph

Cited by 7 publications

References 1 publication

Traditional Chinese Medicine Automated Diagnosis Based on Knowledge Graph Reasoning

Traditional Chinese Medicine Automated Diagnosis Based on Knowledge Graph Reasoning

Medical Knowledge Graph in Chinese Using Deep Semantic Mobile Computation Based on IoT and WoT

State-element-aware syndrome classification based on hypergraph convolutional network

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